Resource Allocation for Multi-user Mobile-edge Computing Systems with Delay Constraints

The computation offloading in mobile-edge computing (MEC) systems emerges as a promising technology to enhance users' quality-of-experience over mobile devices (MDs). However, the design of computation offloading policy for MEC systems inevitably faces challenges with respect to the gap between dynamic task generation in MDs and the limited resources at an MEC server, especially for a multi-user MEC system. More specifically, whether or not offload a task to a nearby MEC server and how much communication and computing resources are allocated to the selected MDs should be carefully investigated to optimize the long-term system performance. In this paper, we handle this issue based on the Markov decision process, where collaborated resource allocations are determined according to both the queueing state of the task buffer at the MDs and the MEC server. By analyzing the average task delay of each user and the average throughput of the system, we formulate a throughput maximization problem with the constraints on delay, spectrum resource, and computing resource, and develop a throughput-optimal resource allocation policy. Simulation results show that the proposed joint communication and computing resource allocation policy is highly effective and efficient.